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Home / Syllabus / B.Sc. (Data Science) / Fourth Year BSc Data Science Honours / Semester 7 / Essential Technologies in Data Science

B.Sc. (Data Science) Essential Technologies in Data Science Syllabus - Mumbai University

This is the Fourth Year BSc Data Science Honours syllabus under NEP 2020, phased in one year at a time, from the academic year 2024-25. The University still sets the earlier Choice Based papers alongside it for ATKT candidates, so check which scheme your exam form names before you revise.

Essential Technologies in Data Science Syllabus.pdf
Major · Semester 7 · Fourth Year BSc Data Science Honours · 4 credits · 100 marks

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Syllabus for Essential Technologies in Data Science

Major · Semester 7 · Fourth Year BSc Data Science Honours · 4 credits · 100 marks

Module I: Introduction to Data science and Python

  • a Introduction to Data Science, data science life cycle, Applications, and advantages of Python over other programming languages
  • b What is Python? Why Should I learn Python? Installing Python How to execute Python program Writing your first program.
  • c Basic programming elements of Python-variables and constants, identifiers, Typecasting or Type Conversion in Python, indentation, comments, rules of writing identifiers, primitive data types, writing command line programs in python
  • d Operators in Python: Arithmetic operators, relational operators, Logical operators, Membership operators, Taking user input.

Module II: Data structures and control flow

  • a Collection data structures in Python- List, tuples, dictionary, sets and strings
  • b Control flow- Sequential, Branching or Conditional, Iteration or Repetition, Modular or Subroutines Conditional and iteration statements: if elif else statements, loops, for loop and while loops
  • c User defined functions in Python- No Value Pass and No Return, Value Pass and No Return, Value Pass and Return, Function with default arguments, Function with variable arguments, Higher order functions, list comprehension

Module III: Statistics for Data Analysts

  • a Permutations and combinations, probability, Descriptive statistics (mean, median, mode), point estimation, quartiles and boxplot, methods of dispersion, random variables and probability distribution
  • b Measures of shape- skewness, kurtosis, outlier detection, transformation (log, square root)
  • c Inferential statistics- Sampling techniques, Hypothesis testing, Z-score normalization, correlation, ANOVA
  • d Introduction to NumPy, creating NumPy arrays, indexing and slicing, vectorization, Boolean indexing, transformation, inferential statistics using NumPy

Module IV: Data wrangling using Pandas

  • a Introduction to data: NOIR (nominal, Ordinal, Interval and Ratio), continuous and discrete numeric data. Types of data analysis (descriptive, diagnostic, predictive and prescriptive analysis)
  • b Data wrangling using Pandas - Creating Series, Creating Data frame from dictionary, attributes, and method description of a data frame. Drop columns, add columns, add rows, iloc , loc, indexing and slicing data frames, selection with condition, group by summary operation, sorting operations
  • c Introduction to R IDE- components of R IDE, Basic data types in R, Data structures in R, data coercion, importing files, visualisation using ggplot2.
  • d Basic visualisation using matplotlib- Components of a chart, line chart, scatter chart, pie chart, sub plots. 10 Text Books 1. Data Analysis with Pandas and Python by Boris Paskhaver, Manning Publications. Available at: https://www.perlego.com/book/2881120/pandas-in-action-pd 11 Reference Books 1. Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce, Andrew Bruce, Peter Gedeck, O'Reilly Media, 2017 ISBN-10: 1491952962 ISBN-13: 978- 1491952962 2. Foundations of Statistics for Data Scientists With R and Python By Alan Agresti, Maria Kateri, CRC Press Taylor and Francis group, 2022 12 Internal Continuous Assessment: 50% Semester End Examination: 50%

Text Books

  • 1 Data Analysis with Pandas and Python by Boris Paskhaver, Manning Publications. Available at: https://www.perlego.com/book/2881120/pandas-in-action-pd
  • 1 Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce, Andrew Bruce, Peter Gedeck, O'Reilly Media, 2017 ISBN-10: 1491952962 ISBN-13: 978- 1491952962
  • 2 Foundations of Statistics for Data Scientists With R and Python By Alan Agresti, Maria Kateri, CRC Press Taylor and Francis group, 2022

Reproduced from the University of Mumbai syllabus for B.Sc. (Data Science) under NEP 2020. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 2024 25 DS SEM I & II NEP NEP 2020 syllabus, in force from 2024-25 Read full PDF Read
PDF 2023 24 BSc Data Science Sem V & VI Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
PDF 2021 22 BSc Data Science Sem III & IV Earlier Choice Based syllabus, still set for ATKT candidates Read full PDF Read
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